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zhuwq0 commited on
Commit
0f141f9
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1 Parent(s): 5d1d228

add csv files

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Files changed (9) hide show
  1. .gitattributes +1 -0
  2. events.csv +3 -0
  3. events_test.csv +3 -0
  4. events_train.csv +3 -0
  5. merge_dataset.py +81 -0
  6. picks.csv +3 -0
  7. picks_test.csv +3 -0
  8. picks_train.csv +3 -0
  9. test.ipynb +326 -0
.gitattributes CHANGED
@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
 
 
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
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+ *.csv filter=lfs diff=lfs merge=lfs -text
events.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:c0a03eb57ce05865f260347d0edebb60a42eb6bf3e5587d35e6f2cdd9130bc32
3
+ size 93911614
events_test.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:67701af6a085c255ae7a9c387d732370318437d20845231b4883e0a73d14f011
3
+ size 3527362
events_train.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:081dd79513099b624fc1faebf3a9c8cee482907b6840790e02bd1a7adb621d91
3
+ size 90384389
merge_dataset.py ADDED
@@ -0,0 +1,81 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # %%
2
+ import os
3
+
4
+ import h5py
5
+ import matplotlib.pyplot as plt
6
+ from tqdm import tqdm
7
+ import pandas as pd
8
+
9
+
10
+ # %%
11
+ h5_dirs = ["./quakeflow_nc/waveform_h5", "./quakeflow_sc/waveform_h5"]
12
+ h5_out = "waveform.h5"
13
+ h5_train = "waveform_train.h5"
14
+ h5_test = "waveform_test.h5"
15
+
16
+ # # %%
17
+ # h5_dir = "waveform_h5"
18
+ # h5_out = "waveform.h5"
19
+ # h5_train = "waveform_train.h5"
20
+ # h5_test = "waveform_test.h5"
21
+
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+ h5_file_lists = [sorted(os.listdir(h5_dir)) for h5_dir in h5_dirs]
23
+ train_file_lists = [x[:-1] for x in h5_file_lists]
24
+ test_file_lists = [x[-1:] for x in h5_file_lists]
25
+ # train_files = h5_files
26
+ # train_files = [x for x in train_files if (x != "2014.h5") and (x not in [])]
27
+ # test_files = []
28
+ print(f"train files: {train_file_lists}")
29
+ print(f"test files: {test_file_lists}")
30
+
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+ # %%
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+ # %%
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+ with h5py.File(h5_out, "w") as fp:
34
+ # external linked file
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+ for h5_dir, h5_files in zip(h5_dirs, h5_file_lists):
36
+ for h5_file in h5_files:
37
+ with h5py.File(os.path.join(h5_dir, h5_file), "r") as f:
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+ for event in tqdm(f.keys(), desc=h5_file, total=len(f.keys())):
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+ if event not in fp:
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+ fp[event] = h5py.ExternalLink(os.path.join(h5_dir, h5_file), event)
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+ else:
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+ print(f"{event} already exists")
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+ continue
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+
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+ # %%
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+ with h5py.File(h5_train, "w") as fp:
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+ # external linked file
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+ for h5_dir, h5_files in zip(h5_dirs, train_file_lists):
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+ for h5_file in h5_files:
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+ with h5py.File(os.path.join(h5_dir, h5_file), "r") as f:
51
+ for event in tqdm(f.keys(), desc=h5_file, total=len(f.keys())):
52
+ if event not in fp:
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+ fp[event] = h5py.ExternalLink(os.path.join(h5_dir, h5_file), event)
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+ else:
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+ print(f"{event} already exists")
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+ continue
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+
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+ # %%
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+ with h5py.File(h5_test, "w") as fp:
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+ # external linked file
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+ for h5_dir, h5_files in zip(h5_dirs, test_file_lists):
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+ for h5_file in h5_files:
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+ with h5py.File(os.path.join(h5_dir, h5_file), "r") as f:
64
+ for event in tqdm(f.keys(), desc=h5_file, total=len(f.keys())):
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+ if event not in fp:
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+ fp[event] = h5py.ExternalLink(os.path.join(h5_dir, h5_file), event)
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+ else:
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+ print(f"{event} already exists")
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+ continue
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+
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+ dirs = ["./quakeflow_nc", "./quakeflow_sc"]
72
+ csv_files = ['events.csv', 'events_test.csv', 'events_train.csv', 'picks.csv', 'picks_test.csv', 'picks_train.csv']
73
+
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+ for csv_file in csv_files:
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+ dfs = []
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+ for dir in dirs:
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+ df = pd.read_csv(f"{dir}/{csv_file}")
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+ dfs.append(df)
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+ df = pd.concat(dfs)
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+ df.to_csv(csv_file, index=False, na_rep='')
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+
picks.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:49b16168d50ea45239e0588c193767d3c7eb3c05ce631f8843eee7b966a57310
3
+ size 4424373898
picks_test.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:29397421dbd6b65ec462014ae7f46ea58ba3b3a215913314de489a41fa9a5fdd
3
+ size 247868475
picks_train.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:ee775cf7b6e10504d8b29cf78b5a5d3151f8c5d301b5aae1b8ff89e7d6255988
3
+ size 4176505544
test.ipynb ADDED
@@ -0,0 +1,326 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 4,
6
+ "metadata": {},
7
+ "outputs": [
8
+ {
9
+ "name": "stdout",
10
+ "output_type": "stream",
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+ "text": [
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+ "train files: [['1987.h5', '1988.h5', '1989.h5', '1990.h5', '1991.h5', '1992.h5', '1993.h5', '1994.h5', '1995.h5', '1996.h5', '1997.h5', '1998.h5', '1999.h5', '2000.h5', '2001.h5', '2002.h5', '2003.h5', '2004.h5', '2005.h5', '2006.h5', '2007.h5', '2008.h5', '2009.h5', '2010.h5', '2011.h5', '2012.h5', '2013.h5', '2014.h5', '2015.h5', '2016.h5', '2017.h5', '2018.h5', '2019.h5', '2020.h5', '2021.h5', '2022.h5'], ['1999.h5', '2000.h5', '2001.h5', '2002.h5', '2003.h5', '2004.h5', '2005.h5', '2006.h5', '2007.h5', '2008.h5', '2009.h5', '2010.h5', '2011.h5', '2012.h5', '2013.h5', '2014.h5', '2015.h5', '2016.h5', '2017.h5', '2018.h5', '2019.h5', '2020.h5', '2021.h5', '2022.h5']]\n",
13
+ "test files: [['2023.h5'], ['2023.h5']]\n"
14
+ ]
15
+ }
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+ ],
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+ "source": [
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+ "# %%\n",
19
+ "import os\n",
20
+ "\n",
21
+ "import h5py\n",
22
+ "import matplotlib.pyplot as plt\n",
23
+ "from tqdm import tqdm\n",
24
+ "\n",
25
+ "# %%\n",
26
+ "h5_dirs = [\"NC/waveform_h5\", \"SC/waveform_h5\"]\n",
27
+ "h5_out = \"waveform.h5\"\n",
28
+ "h5_train = \"waveform_train.h5\"\n",
29
+ "h5_test = \"waveform_test.h5\"\n",
30
+ "\n",
31
+ "# # %%\n",
32
+ "# h5_dir = \"waveform_h5\"\n",
33
+ "# h5_out = \"waveform.h5\"\n",
34
+ "# h5_train = \"waveform_train.h5\"\n",
35
+ "# h5_test = \"waveform_test.h5\"\n",
36
+ "\n",
37
+ "h5_file_lists = [sorted(os.listdir(h5_dir)) for h5_dir in h5_dirs]\n",
38
+ "train_file_lists = [x[:-1] for x in h5_file_lists]\n",
39
+ "test_file_lists = [x[-1:] for x in h5_file_lists]\n",
40
+ "# train_files = h5_files\n",
41
+ "# train_files = [x for x in train_files if (x != \"2014.h5\") and (x not in [])]\n",
42
+ "# test_files = []\n",
43
+ "print(f\"train files: {train_file_lists}\")\n",
44
+ "print(f\"test files: {test_file_lists}\")"
45
+ ]
46
+ },
47
+ {
48
+ "cell_type": "code",
49
+ "execution_count": 5,
50
+ "metadata": {},
51
+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "1987.h5: 100%|██████████| 21/21 [00:00<00:00, 13205.45it/s]\n",
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+ "1988.h5: 100%|██████████| 122/122 [00:00<00:00, 44093.50it/s]\n",
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+ "1989.h5: 100%|██████████| 145/145 [00:00<00:00, 48487.13it/s]\n",
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+ "1990.h5: 100%|██████████| 149/149 [00:00<00:00, 47098.60it/s]\n",
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+ "1991.h5: 100%|██████████| 161/161 [00:00<00:00, 42155.12it/s]\n",
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+ "2008.h5: 100%|██████████| 11822/11822 [00:00<00:00, 43993.57it/s]\n",
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+ "2021.h5: 100%|██████████| 15206/15206 [00:00<00:00, 35751.59it/s]\n",
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+ "2022.h5: 100%|██████████| 12532/12532 [00:00<00:00, 34850.17it/s]\n",
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+ "2023.h5: 100%|██████████| 13410/13410 [00:00<00:00, 34365.93it/s]\n",
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+ "1987.h5: 100%|██████████| 21/21 [00:00<00:00, 42184.09it/s]\n",
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+ "1988.h5: 100%|██████████| 122/122 [00:00<00:00, 54774.68it/s]\n",
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+ "1989.h5: 100%|██████████| 145/145 [00:00<00:00, 53522.32it/s]\n",
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+ "1990.h5: 100%|██████████| 149/149 [00:00<00:00, 55506.82it/s]\n",
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+ "1991.h5: 100%|██████████| 161/161 [00:00<00:00, 51115.20it/s]\n",
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+ "1992.h5: 100%|██████████| 158/158 [00:00<00:00, 51384.05it/s]\n",
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+ "1993.h5: 100%|██████████| 1647/1647 [00:00<00:00, 54106.28it/s]\n",
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+ "1994.h5: 100%|██████████| 1625/1625 [00:00<00:00, 55482.02it/s]\n",
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+ "1995.h5: 100%|██████████| 4595/4595 [00:00<00:00, 58728.18it/s]\n",
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+ "1996.h5: 100%|██████████| 4287/4287 [00:00<00:00, 55312.14it/s]\n",
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+ "1997.h5: 100%|██████████| 6260/6260 [00:00<00:00, 52698.71it/s]\n",
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+ "1998.h5: 100%|██████████| 4568/4568 [00:00<00:00, 53222.39it/s]\n",
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+ "1999.h5: 100%|██████████| 6024/6024 [00:00<00:00, 52687.69it/s]\n",
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+ "2000.h5: 100%|██████████| 1105/1105 [00:00<00:00, 58423.12it/s]\n",
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+ "2001.h5: 100%|██████████| 2679/2679 [00:00<00:00, 57428.62it/s]\n",
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+ "2002.h5: 100%|██████████| 7506/7506 [00:00<00:00, 58583.76it/s]\n",
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+ "2003.h5: 100%|██████████| 8420/8420 [00:00<00:00, 54412.15it/s]\n",
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+ "2004.h5: 100%|██████████| 15232/15232 [00:00<00:00, 57007.87it/s]\n",
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+ "2005.h5: 100%|██████████| 8465/8465 [00:00<00:00, 54145.10it/s]\n",
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+ "2006.h5: 100%|██████████| 7269/7269 [00:00<00:00, 51191.79it/s]\n",
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+ "2007.h5: 100%|██████████| 12722/12722 [00:00<00:00, 47122.06it/s]\n",
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+ "2009.h5: 100%|██████████| 11412/11412 [00:00<00:00, 43242.28it/s]\n",
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+ "2010.h5: 100%|██████████| 11973/11973 [00:00<00:00, 42805.26it/s]\n",
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+ "2011.h5: 100%|██████████| 13848/13848 [00:00<00:00, 42280.57it/s]\n",
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+ "2012.h5: 100%|██████████| 15738/15738 [00:00<00:00, 41685.79it/s]\n",
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+ "2013.h5: 100%|██████████| 16020/16020 [00:00<00:00, 43329.98it/s]\n",
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+ "2014.h5: 100%|██████████| 21804/21804 [00:00<00:00, 44983.11it/s]\n",
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+ "2015.h5: 100%|██████████| 14230/14230 [00:00<00:00, 44135.09it/s]\n",
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+ "2016.h5: 100%|██████████| 13977/13977 [00:00<00:00, 44137.57it/s]\n",
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+ "2017.h5: 100%|██████████| 18099/18099 [00:00<00:00, 42492.87it/s]\n",
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+ "2018.h5: 100%|██████████| 14036/14036 [00:00<00:00, 39025.89it/s]\n",
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+ "2019.h5: 100%|██████████| 15064/15064 [00:00<00:00, 38635.81it/s]\n",
151
+ "2020.h5: 100%|██████████| 16155/16155 [00:00<00:00, 38816.08it/s]\n",
152
+ "2021.h5: 100%|██████████| 15581/15581 [00:00<00:00, 39043.39it/s]\n",
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+ "2022.h5: 100%|██████████| 10898/10898 [00:00<00:00, 39888.76it/s]\n",
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+ "1999.h5: 100%|██████████| 24/24 [00:00<00:00, 34532.86it/s]\n",
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+ "2000.h5: 100%|██████████| 27/27 [00:00<00:00, 26690.13it/s]\n",
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+ "2001.h5: 100%|██████████| 29/29 [00:00<00:00, 30700.36it/s]\n",
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+ "2002.h5: 100%|██████████| 34/34 [00:00<00:00, 35029.80it/s]\n",
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+ "2003.h5: 100%|██████████| 73/73 [00:00<00:00, 35970.89it/s]\n",
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+ "2004.h5: 100%|██████████| 740/740 [00:00<00:00, 36212.21it/s]\n",
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+ "2005.h5: 100%|██████████| 1249/1249 [00:00<00:00, 40190.00it/s]\n",
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+ "2006.h5: 100%|██████████| 657/657 [00:00<00:00, 38375.36it/s]\n",
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+ "2007.h5: 100%|██████████| 1743/1743 [00:00<00:00, 37327.53it/s]\n",
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+ "2008.h5: 100%|██████████| 12570/12570 [00:00<00:00, 41988.87it/s]\n",
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+ "2009.h5: 100%|██████████| 14282/14282 [00:00<00:00, 42669.64it/s]\n",
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+ "2010.h5: 100%|██████████| 34818/34818 [00:00<00:00, 42444.23it/s]\n",
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+ "2011.h5: 100%|██████████| 13427/13427 [00:00<00:00, 37659.90it/s]\n",
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+ "2012.h5: 100%|██████████| 15416/15416 [00:00<00:00, 37632.25it/s]\n",
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+ "2013.h5: 100%|██████████| 17056/17056 [00:00<00:00, 37530.78it/s]\n",
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+ "2014.h5: 100%|██████████| 13937/13937 [00:00<00:00, 37437.27it/s]\n",
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+ "2015.h5: 100%|██████████| 15311/15311 [00:00<00:00, 39536.43it/s]\n",
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+ "2016.h5: 100%|██████████| 15470/15470 [00:00<00:00, 38313.60it/s]\n",
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+ "2017.h5: 100%|██████████| 15771/15771 [00:00<00:00, 40890.63it/s]\n",
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+ "2018.h5: 100%|██████████| 20344/20344 [00:00<00:00, 38624.25it/s]\n",
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+ "2019.h5: 100%|██████████| 60424/60424 [00:01<00:00, 35660.70it/s]\n",
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+ "2020.h5: 100%|██████████| 33621/33621 [00:00<00:00, 36877.49it/s]\n",
176
+ "2021.h5: 100%|██████████| 15206/15206 [00:00<00:00, 36150.85it/s]\n",
177
+ "2022.h5: 100%|██████████| 12532/12532 [00:00<00:00, 37151.34it/s]\n",
178
+ "2023.h5: 100%|██████████| 11085/11085 [00:00<00:00, 55073.48it/s]\n",
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+ "2023.h5: 100%|██████████| 13410/13410 [00:00<00:00, 55802.88it/s]\n"
180
+ ]
181
+ }
182
+ ],
183
+ "source": [
184
+ "# %%\n",
185
+ "with h5py.File(h5_out, \"w\") as fp:\n",
186
+ " # external linked file\n",
187
+ " for h5_dir, h5_files in zip(h5_dirs, h5_file_lists):\n",
188
+ " for h5_file in h5_files:\n",
189
+ " with h5py.File(os.path.join(h5_dir, h5_file), \"r\") as f:\n",
190
+ " for event in tqdm(f.keys(), desc=h5_file, total=len(f.keys())):\n",
191
+ " if event not in fp:\n",
192
+ " fp[event] = h5py.ExternalLink(os.path.join(h5_dir, h5_file), event)\n",
193
+ " else:\n",
194
+ " print(f\"{event} already exists\")\n",
195
+ " continue\n",
196
+ "\n",
197
+ "# %%\n",
198
+ "with h5py.File(h5_train, \"w\") as fp:\n",
199
+ " # external linked file\n",
200
+ " for h5_dir, h5_files in zip(h5_dirs, train_file_lists):\n",
201
+ " for h5_file in h5_files:\n",
202
+ " with h5py.File(os.path.join(h5_dir, h5_file), \"r\") as f:\n",
203
+ " for event in tqdm(f.keys(), desc=h5_file, total=len(f.keys())):\n",
204
+ " if event not in fp:\n",
205
+ " fp[event] = h5py.ExternalLink(os.path.join(h5_dir, h5_file), event)\n",
206
+ " else:\n",
207
+ " print(f\"{event} already exists\")\n",
208
+ " continue\n",
209
+ "\n",
210
+ "# %%\n",
211
+ "with h5py.File(h5_test, \"w\") as fp:\n",
212
+ " # external linked file\n",
213
+ " for h5_dir, h5_files in zip(h5_dirs, test_file_lists):\n",
214
+ " for h5_file in h5_files:\n",
215
+ " with h5py.File(os.path.join(h5_dir, h5_file), \"r\") as f:\n",
216
+ " for event in tqdm(f.keys(), desc=h5_file, total=len(f.keys())):\n",
217
+ " if event not in fp:\n",
218
+ " fp[event] = h5py.ExternalLink(os.path.join(h5_dir, h5_file), event)\n",
219
+ " else:\n",
220
+ " print(f\"{event} already exists\")\n",
221
+ " continue\n",
222
+ "\n",
223
+ "# %%\n"
224
+ ]
225
+ },
226
+ {
227
+ "cell_type": "code",
228
+ "execution_count": 6,
229
+ "metadata": {},
230
+ "outputs": [],
231
+ "source": [
232
+ "import h5py"
233
+ ]
234
+ },
235
+ {
236
+ "cell_type": "code",
237
+ "execution_count": 10,
238
+ "metadata": {},
239
+ "outputs": [
240
+ {
241
+ "data": {
242
+ "text/plain": [
243
+ "653073"
244
+ ]
245
+ },
246
+ "execution_count": 10,
247
+ "metadata": {},
248
+ "output_type": "execute_result"
249
+ }
250
+ ],
251
+ "source": [
252
+ "h5_fp = h5py.File(\"waveform.h5\", \"r\")\n",
253
+ "len(list(h5_fp.keys()))"
254
+ ]
255
+ },
256
+ {
257
+ "cell_type": "code",
258
+ "execution_count": 11,
259
+ "metadata": {},
260
+ "outputs": [
261
+ {
262
+ "name": "stderr",
263
+ "output_type": "stream",
264
+ "text": [
265
+ "/tmp/ipykernel_3422288/108058564.py:9: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
266
+ " df = pd.read_csv(f\"{dir}/{csv_file}\")\n",
267
+ "/tmp/ipykernel_3422288/108058564.py:9: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
268
+ " df = pd.read_csv(f\"{dir}/{csv_file}\")\n",
269
+ "/tmp/ipykernel_3422288/108058564.py:9: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
270
+ " df = pd.read_csv(f\"{dir}/{csv_file}\")\n"
271
+ ]
272
+ }
273
+ ],
274
+ "source": [
275
+ "import pandas as pd\n",
276
+ "\n",
277
+ "dirs = [\"NC\", \"SC\"]\n",
278
+ "csv_files = ['events.csv', 'events_test.csv', 'events_train.csv', 'picks.csv', 'picks_test.csv', 'picks_train.csv']\n",
279
+ "\n",
280
+ "for csv_file in csv_files:\n",
281
+ " dfs = []\n",
282
+ " for dir in dirs:\n",
283
+ " df = pd.read_csv(f\"{dir}/{csv_file}\")\n",
284
+ " dfs.append(df)\n",
285
+ " df = pd.concat(dfs)\n",
286
+ " df.to_csv(csv_file, index=False, na_rep='')"
287
+ ]
288
+ },
289
+ {
290
+ "cell_type": "code",
291
+ "execution_count": null,
292
+ "metadata": {},
293
+ "outputs": [],
294
+ "source": [
295
+ "scp [email protected]:/data/wanghy/events_test.csv ./\n",
296
+ "scp [email protected]:/data/wanghy/picks.csv ./\n",
297
+ "scp [email protected]:/data/wanghy/picks_train.csv ./\n",
298
+ "scp [email protected]:/data/wanghy/picks_test.csv ./\n",
299
+ "scp [email protected]:/data/wanghy/waveform.h5 ./\n",
300
+ "scp [email protected]:/data/wanghy/waveform_test.h5 ./\n",
301
+ "scp [email protected]:/data/wanghy/waveform_train.h5 ./"
302
+ ]
303
+ }
304
+ ],
305
+ "metadata": {
306
+ "kernelspec": {
307
+ "display_name": "obspy",
308
+ "language": "python",
309
+ "name": "python3"
310
+ },
311
+ "language_info": {
312
+ "codemirror_mode": {
313
+ "name": "ipython",
314
+ "version": 3
315
+ },
316
+ "file_extension": ".py",
317
+ "mimetype": "text/x-python",
318
+ "name": "python",
319
+ "nbconvert_exporter": "python",
320
+ "pygments_lexer": "ipython3",
321
+ "version": "3.11.10"
322
+ }
323
+ },
324
+ "nbformat": 4,
325
+ "nbformat_minor": 2
326
+ }